Effective Machine Learning Teams Best Practices for Ml Practitioners

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Effective Machine Learning Teams Best Practices for Ml Practitioners

Effective Machine Learning Teams Best Practices for Ml Practitioners

Effective Machine Learning Teams Best Practices for Ml Practitioners

Effective Machine Learning Teams Best Practices for Ml Practitioners

Effective Machine Learning Teams Best Practices for Ml Practitioners

Effective Machine Learning Teams Best Practices for Ml Practitioners

Effective Machine Learning Teams Best Practices for Ml Practitioners

Effective Machine Learning Teams Best Practices for Ml Practitioners

Effective Machine Learning Teams Best Practices for Ml Practitioners

Effective Machine Learning Teams Best Practices for Ml Practitioners

Effective Machine Learning Teams Best Practices for Ml Practitioners

Effective Machine Learning Teams Best Practices for Ml Practitioners

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Effective Machine Learning Teams Best Practices for Ml Practitioners

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Mô tả

Effective Machine Learning Teams Best Practices for Ml Practitioners

Sách gia công, Bìa mềm

Gain the valuable skills and techniques you need to

accelerate the delivery of machine learning solutions. With this

practical guide, data scientists and ML engineers will learn how to

bridge the gap between data science and Lean software delivery in a

practical and simple way. David Tan and Ada Leung from Thoughtworks show

you how to apply time-tested software engineering skills and Lean

delivery practices that will improve your effectiveness in ML projects.

Based

on the authors’ experience across multiple real-world data and ML

projects, the proven techniques in this book will help teams avoid

common traps in the ML world, so you can iterate more quickly and

reliably. With these techniques, data scientists and ML engineers can

overcome friction and experience flow when delivering machine learning

solutions.

This book shows you how to:

• Apply engineering

practices such as writing automated tests, containerizing development

environments, and refactoring problematic code bases

• Apply MLOps and CI/CD practices to accelerate experimentation cycles and improve reliability of ML solutions

• Design maintainable and evolvable ML solutions that allow you to respond to changes in an agile fashion

• Apply delivery and product practices to iteratively improve your odds of building the right product for your users

• Use intelligent code editor features to code more effectively

Year:2024

Edition:1

Publisher:O'Reilly Media / O'Reilly & Associates Inc.

Language:english

Pages:402

https://zalo.me/0969436832

0969436832